Triple
T5749645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naoko Mori |
E126818
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Naoko |
E318706
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Naoko | Statement: [Naoko Mori, givenName, Naoko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naoko Context triple: [Naoko Mori, givenName, Naoko]
-
A.
Naoko
chosen
Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
-
B.
Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
-
C.
Atsuko
Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
-
D.
Masako
Masako is the Empress of Japan, a former diplomat and Harvard-educated member of the Imperial House known for her international background and public role.
-
E.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c00832aedc81909899801b141fa3b4 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0288870fc819080e883c9d589359b |
completed | March 22, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c124e9ee44819095a62faa35c308ec |
completed | March 23, 2026, 11:32 a.m. |
Created at: March 22, 2026, 3:48 p.m.